Parallel Single-input Single-output Research Articles

Low-cost relay selection in multihop cooperative networks

A best relay selection algorithm for a cooperative multi-hop cross-layer single-input single-output (SISO) amplify-and-forward (AF) wireless relay network is analyzed in this work, with the application where exact channel state information (CSI) is known. We present algorithmic strategies to simplify a multi-hop parallel SISO relay network into a series multi-hop network by finding the best path having the maximum received signal-to-noise ratio (SNR). The best relay selection by using dynamic programming search entails high computations and large memory requirements, as well as involves the full CSI information, making this approach impractical for large-scale networks. The goal of the proposed low-cost near-optimal routing strategy in this work is to provide close to optimal performance with much less complexity compared to traditional routing. Next, we also propose a low-cost power allocation to further improve system performance over traditional power allocation found in the literature. Computer simulations show excellent performance improvement of our proposed methods in terms of bit error rate (BER) as well as outage probability over the traditional relaying and power allocation algorithms.

Decoupled beamforming techniques for distributed MIMO two-way multi-relay networks with imperfect CSI

This paper investigates decoupled beamforming techniques for the distributed multi-input multi-output (MIMO) two-way relay networks (TWRN) with imperfect channel state informations (CSIs). The objective of this paper is to maximize the weighted sum rate (SR) with semi-infinite relay power constraints. Since the objective problem is difficult to be solved directly, considering the high signal-to-residual-interference-plus-noise ratio (SRINR) and employing the Cauchy-Schwarz inequality and S-lemma, the problem can be approximately converted into a source beamforming decoupled one. In addition, with the optimal relay beamforming design and maximum ratio combining (MRC) at the receiver, the MIMO channels are decoupled into parallel single-input single-output (SISO) channels. By this way, the suboptimal relay beamforming matrix can be efficiently obtained with minimal relay power constraint. Specifically, the semi-infinite constraints can be reformulated into a linear matrix inequality (LMI), which can be efficiently solved by using an alternating optimization algorithm. Numerical results demonstrate that our proposed decoupled beamforming scheme outperforms the existing works in terms of the SR and the computational complexity with satisfactory convergence.

How to Diagonalize a MIMO Channel With Arbitrary Transmit Covariance?

Multiple-input multiple-output (MIMO) or multi-antenna communication is a key technique to achieve high spectral efficiency in wireless systems. For the point-to-point MIMO channel, it is a well-known result that the channel singular value decomposition (SVD) based linear precoding and decoding achieves the channel capacity, which also diagonalizes the MIMO channel into parallel single-input single-output (SISO) sub-channels for independent encoding and decoding. However, in multi-user MIMO systems, the optimal transmit covariance of each MIMO link is generally not its channel SVD based as a result of the control and balance of the co-channel interference among users. Thus, it remains unknown whether the linear precoding/decoding strategy is still able to achieve the capacity of each MIMO link and yet diagonalize its MIMO channel, with a given set of optimal transmit covariance of all users. This letter solves this open problem by providing a closed-form capacity-achieving linear precoder/decoder design that diagonalizes a MIMO channel with arbitrary transmit covariance. Numerical examples are also provided to validate the proposed solution in various multi-user MIMO systems.

Decode-and-Forward Relaying via Standard AWGN Coding and Decoding

A framework is developed for decode-and-forward-based relaying using standard coding and decoding that are good for the single-input single-output (SISO) additive white Gaussian noise channel. The framework is applicable to various scenarios and is demonstrated for several important cases. Each of these scenarios is transformed into an equivalent Gaussian multiple-input multiple-output (MIMO) common-message broadcast problem, which proves useful even when all links are SISO ones. Over the effective MIMO broadcast channel, a recently developed Gaussian MIMO common-message broadcast scheme is applied. This scheme transforms the MIMO links into a set of parallel SISO channels with no loss of mutual information, using linear pre- and post-processing combined with successive decoding. Over these resulting SISO channels, off-the-shelf scalar codes may be used.

Adaptive Resource Allocation in MIMO-OFDMA based on Subcarrier Allocation (SA) and (GA)

In recent years, the eyes of scholars has been on the road to an proficient allocation scheme of signalling dimension between users in both uplink and downlink channels, since bandwidth is usually limited and/or very expensive. This paper covenants with the rate adaptive MIMO-OFDMA (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) downlink resource allocation scheme. It has an objective of maximizing the total system capacity subject to constraints on total power, bit error rate and proportional fairness. The proposed algorithm determines the number of subcarriers for each user and then handing over the subcarrier to each user. Two optimization methods, one based on Subcarrier Allocation (SA) and the other, based on Genetic Algorithm (GA) are used for resource allocation. In the optimization technique based on SA, using SVD (Singular Value Decomposition), the MIMO fading channel of each subcarrier is transformed into an equivalent bank of parallel SISO (Single Input Single Output) subchannels, which leads to a better trade-off strategy among system capacity, computational complexity and proportional fairness. Paralleled to static allocation schemes, the new algorithm has significantly lower complexity and in simulation fruitages higher system capacity. Simulation results show that the subcarrier allocation based on GA can provide capacity improvement with the reduction in fairness.

Low-Complexity Iterative Channel Estimation for Turbo Receivers

This letter discusses a receiver-side channel estimation algorithm well-suited to turbo equalizers for multiple-input multiple-output (MIMO) systems. The proposed technique is a Kalman-based channel estimator that runs on parallel single-input single-output (SISO) channels. Soft-decision-feedback interference cancellation is utilized to reduce the MIMO channel estimation problem into multiple SISO channel estimation problems. Unlike existing methods, however, the inherent correlation that exists among the output samples of the successive interference canceller is suppressed via careful puncturing of observation samples. The quality of soft decisions and channel estimates are also continuously monitored and incorporated in the Kalman filter update process.

A Unified Framework for Optimizing Linear Nonregenerative Multicarrier MIMO Relay Communication Systems

In this paper, we develop a unified framework for linear nonregenerative multicarrier multiple-input multiple-output (MIMO) relay communications in the absence of the direct source-destination link. This unified framework classifies most commonly used design objectives such as the minimal mean-square error and the maximal mutual information into two categories: Schur-concave and Schur-convex functions. We prove that for Schur-concave objective functions, the optimal source precoding matrix and relay amplifying matrix jointly diagonalize the source-relay-destination channel matrix and convert the multicarrier MIMO relay channel into parallel single-input single-output (SISO) relay channels. While for Schur-convex objectives, such joint diagonalization occurs after a specific rotation of the source precoding matrix. After the optimal structure of the source and relay matrices is determined, the linear nonregenerative relay design problem boils down to the issue of power loading among the resulting SISO relay channels. We show that this power loading problem can be efficiently solved by an alternating technique. Numerical examples demonstrate the effectiveness of the proposed framework.

A comparison of TDMA, dirty paper coding, and beamforming for multiuser MIMO relay networks

A two-hop multiple-input multiple-output (MIMO) relay network which comprises a multiple antenna source, an amplify-and-forward MIMO relay and many potential users are studied in this paper. Consider the achievable sum rate as the performance metric, a joint design method for the processing units of the BS and relay node is proposed. The optimal structures are given, which decompose the multiuser MIMO relay channel into several parallel single-input single-output relay channels. With these structures, the signal-to-noise ratio at the destination users is derived; and the power allocation is proved to be a convex problem. We also show that high sum rate can be achieved by pairing each link according to its magnitude. The sum rate of three broadcast strategies, time division multiple access (TDMA) to the strongest user, dirty paper coding (DPC), and beamforming (BF) are investigated. The sum rate bounds of these strategies and the sum capacity (achieved by DPC) gain over TDMA and BF are given. With these results, it can be easily obtained that how far away TDMA and BF are from being optimal in terms of the achievable sum rate.